Post on 05-Nov-2019
Nº 17/03 February 2017
WORKING PAPER
DiGiX: The Digitization Index Noelia Cámara and David Tuesta
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17/03 Working Paper February 2017
DiGiX: the Digitization Index
Preliminary draft. Please, do not cite without permission
Noelia Cámara and David Tuesta
November 2016
Abstract
The Digitization Index (DiGiX) assesses the factors, agents’ behavior and institutions that enable a country to
fully leverage Information and Communication Technologies (ICTs) for increased competitiveness and well-
being. It is a composite index that summarizes relevant indicators on 100 countries’ digital performance. The
DiGiX is structured around six principal dimensions: infrastructure, households’ adoption, enterprises’
adoption, costs, regulation and contents. Each dimension is in turn divided into a number of individual
indicators, adding up to a total of 21.
Keywords: digitization, principal component analysis, Internet
JEL classification: C43, O3.
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1. Introduction
The digital economy is an essential part of the architecture of the Fourth Industrial Revolution. The Digitization
Index (DiGiX) assesses the factors, agents’ behaviour and institutions that enable a country to fully leverage
Information and Communication Technologies (ICTs) for increased competitiveness and well-being. It is a
composite index that summarizes relevant indicators on 100 countries’ digital performance. The DiGiX is
structured around six dimensions: infrastructure, households’ adoption, enterprises’ adoption, costs, regulation
and contents. Each dimension is in turn divided into a number of individual indicators, adding up to a total of 21.
There are two main approaches in the literature to measure the degree of digitization at country level. Firstly,
the Networked Readiness Index (NRI) created by the World Economic Forum and measures the propensity for
countries to exploit the opportunities offered by information and communications technology. It measures, the
performance of 139 economies in leveraging information and communications technologies to boost
competitiveness, innovation and well-being. The NRI the computation of the overall NRI score is based on
successive aggregations by simple averaging of scores: individual indicators are aggregated to obtain pillar
scores, which are then combined to obtain subindex scores. Subindex scores are in turn combined to produce
a country’s overall NRI score. Secondly, the Digital Economy and Society Index (DESI) developed by
European Commission is a composite index that summarizes relevant indicators on Europe’s digital
performance and tracks the evolution of EU member states in digital competitiveness. The overall index and
dimensions, weights are attributed exogenously and reflect the European Union digital policy priorities.
We build on this literature and propose the DiGiX. Our contribution to the literature is in several ways. First, we
assign the weights endogenously, according to the data structure. Second, we create an index that captures a
narrower concept of digitization. Third, we create an index that is robust to redundant information.
The rest of the paper is organized as follows. Section 2 presents the structure of the DiGiX 2016. Section 3
shows the methodology. Section 4 presents the results for 2016 and Section 5 summarizes the main findings.
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2. Data and index structure
We measure digitization based on demand and supply. The structure of the index is as follows. There are 21
indicators that are divided in 6 dimensions or subindices: infrastructure, costs, regulation, contents,
households’ adoption and enterprises’ adoption. At the same time each dimension summarizes information of
several individual indicators (from 1 up to 6). The demand-side information is captured by the so-called output
indicators that correspond to the dimensions: households’, enterprises’ and government adoption (contents).
This group measures the degree of engagement of the different economic agents households, firms and
government with the digitization. The supply-side information is included in the infrastructure, costs and
regulation dimensions. They represent inputs that enable the digitization process.
Regarding the theoretical framework of our definition, this index differs from the other indices in the literature in
the lack of human capital indicators. We choose a narrower definition and only consider variables directly
related to digitization. The human capital is an explanatory variable rather than a variable to define digitization.
The composition of the DiGiX is described in Figure 1 and Table 1 shows descriptive statistics of the 21
indicators.
The indicators are derived from different official sources that are described in the Appendix. DiGiX 2016
summarizes data collected mostly during calendar year 2015.
Figure 1
Digital Index 2016: Composition
Source: BBVA Research
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Table 1
Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max cv
Infraestructure
3G coverage 100 88.80 16.58 27.00 100.00 0.19
Bandwidth (bit/s) per Internet user 100 218137.90 828653.40 992.20 7186378.00 3.80
Secure Internet Servers 100 509.08 782.04 1.32 3406.66 1.54
Bandwidth (bit/s) 100 1907580.00 4250706.00 4800.00 25800000.00 2.23
Users’ adoption
Mobile-broadband subscriptions 100 63.22 33.07 4.27 144.05 0.52
Fixed (wired)-broadband subscriptions 100 17.53 13.04 0.01 44.79 0.74
Virtual social networks 100 5.81 0.55 3.83 6.79 0.09
Households with Internet 100 60.57 27.14 8.58 98.79 0.45
Individuals using the Internet 100 62.60 23.26 14.40 98.20 0.37
Enterprises’ adoption
B2B 100 5.01 0.66 3.37 6.36 0.13
B2C 100 4.82 0.75 2.90 6.30 0.16
Firm-level technology absorption 100 4.91 0.68 3.35 6.17 0.14
Costs
Fixed broadband tariffs 100 36.76 23.02 2.65 157.62 0.63
Internet & telephony competition 100 1.77 0.37 0.25 2.00 0.21
Regulation
Laws relating to ICTs 100 4.25 0.83 2.04 5.94 0.19
Software piracy rate 100 56.68 21.56 18.00 91.00 0.38
Effectiveness of law-making bodies 100 3.78 0.99 1.78 6.16 0.26
Judicial independence 100 4.13 1.32 1.65 6.75 0.32
Efficiency of legal system in settling dispute 100 3.95 0.98 2.02 6.16 0.25
Eff. of legal system in challenging regulations
100 3.59 0.86 1.92 5.57 0.24
Digital content
Government Online Service Index 100 0.57 0.23 0.08 1.00 0.39
Source: BBVA Research
3. Methodology: weights and aggregation methods
We assume that behind a set of correlated variables, we can find an underlying latent structure that can be
identified with a latent variable as is the case of digitization. Two important issues arise in the estimate of any
latent variable: the selection of relevant variables and the estimation of parameters (weights). Regarding the
first issue, it is not possible to rely on standard reduction of information criterion approaches for the selection of
variables. For the second, since digitization is unobserved, standard regression techniques are also unfeasible
to estimate the parameters. The weight assignment to the indicators or sub-indices is critical to maximize the
information from a data set included in an index. A good composite index should comprise important
information from all the indicators, but not be strongly biased towards one or more of these indicators. We
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apply two-stage principal components methodology to estimate the degree of digitization as an indexing
strategy.
Our dataset contains causal variables which summarize the information for digitization. As explained in the
previous section, each causal variable relates to different dimensions that define digitization. The purpose of
dividing the overall set of indicators into three sub-indices is twofold. On the one hand, the three sub-indices
have a meaning so, we get additional disaggregated information that is also useful for policy making. On the
other hand, for methodological purposes, since the sub-indices contain highly inter-correlated indicators, we
estimate the sub-indices first, rather than estimating the overall index directly by picking all the indicators at the
same time. This is a preferred strategy because empirical evidence supports that PCA is biased towards the
weights of indicators which are highly correlated with each other (Mishra, 2007). We minimize this problem by
applying two-stage PCA (Nagar and Basu, 2004). In the first stage, we estimate the six sub-indices:
infrastructure, households’ adoption, enterprises’ adoption, costs, regulation and contents, which defined
digitization. In the second stage, we estimate the dimension weights and the overall DiGiX by using the
dimensions as explanatory variables.
We only retain the information contained in the first component since we consider that it is sufficiently high as
to describe the commonalities that we are looking for. Table 2 shows the explained variance by each
dimension and indicator.
Table 2
% Explained Variance
First component
Infraestructure 47%
Costs 58%
Regulation 80%
Users’ adoption 80%
Enterprises’ adoption 90%
Digital content 100%
BBVA-DiGiX 64%
Source: BBVA Research
Let us postulate that the latent variable DiGiX is linearly determined as follows:
DiGiXi = β1 ∗ Ii + β2 ∗ UAi + β3 ∗ EAi + β4 ∗ Ci + β5 ∗ Ri + β6 ∗ Coi + εi
where subscript i denotes the country, and (I, UA, EA, C, R and CO) capture the dimensions (i.e. infrastructure,
households’ adoption, enterprises’ adoption, costs, regulation and digital contents respectively). Thus, the total
variation in DiGiX is represented by two orthogonal parts: variation due to causal variables and variation due to
error (εi). If the model is well specified, including an adequate number of explanatory variables, we can
reasonably assume that the total variation in DiGiX can be largely explained by the variation in the causal
variables. The relative weights (importance) of each dimension, βj, in the DiGiX are computed as:
βj =∑ λjϕjk j=1
∑ λjj=1
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where λj represents the variance of the jth principal component (weights), for our index, the first component
and k the number of variables in the overall index or in each dimension. Table 3 presents the weights by
indicator and by dimension. Among indicators and dimensions weights are relatively balanced except for the
cost dimension. On the one hand, the two indicators in this dimension have a low variance and then, it is more
difficult to distinguish countries based on this information. On the other hand, the two indicators have a low
correlation so, the common part explained by both indicators is also low.
Table 3
Weights
Infraestructure 18%
3G coverage 21%
Bandwidth (bit/s) per Internet user 26%
Secure Internet Servers 29%
Bandwidth (bit/s) 24%
Costs 9%
Fixed broadband tariffs 50%
Internet & telephony competition 50%
Regulation 18%
Laws relating to ICTs 16%
Software piracy rate 15%
Effectiveness of law-making bodies 17%
Judicial independence 18%
Efficiency of legal system in settling dispute 18%
Eff. of legal system in challenging regulations 17%
Users’ adoption 19%
Mobile-broadband subscriptions 19%
Fixed (wired)-broadband subscriptions 20%
Virtual social networks 19%
Households with Internet 21%
Individuals using the Internet 21%
Enterprises’ adoption 19%
B2B 34%
B2C 33%
Firm-level technology absorption 33%
Digital content 17%
Government Online Service Index 100%
Source: BBVA Research
Finally, we apply a min-max transformation, which preserves the order of, and the relative distance between,
the scores. Each score in the DiGiX is between 0 and 1, with higher values representing better performance.
𝒛𝒊𝒋=
𝒙𝒊𝒋−𝐦𝐢𝐧 (𝒙𝒋)
𝐦𝐚𝐱(𝒙𝒋)−𝐦𝐢𝐧(𝒙𝒋)
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4. Results
The DiGiX finds Luxemburg as the highest-placed country in the world when it comes to digitization. United
Kingdom remains in second place followed by Hong Kong (3rd), United States (4th) and Netherlands (5th.
Making up the rest of the top 10 are the Japan, Singapore, Norway, Finland and Sweden (see Table 4 and
Figure 2). According to our results, these countries might define the technological frontier in terms of
digitization. The highest scores in this index represent the digital frontier. This is a dynamic concept that helps
us to compare countries’ performance in our sample.
When it comes to developing countries, the United Arab Emirates remains at 22nd position and it is the leader
in the Arab world. Bahrain comes in the 26th position and Malaysia in the 29th.They lead the Emerging
Asian ranking mainly, due to strong support received by their governments that are fully committed to the
digital agenda. All the above mentioned some developing countries exhibit higher scores than other developed
countries such as Spain (30th), Portugal (33th) or Italy (52nd).
The performance range by countries in the Latin America and Caribbean region remains widely dispersed with
almost 100 places between Chile (34th) and Nicaragua (98th). Costa Rica, Brazil and Uruguay are the
countries with the best performance in this region, all of them above or close to the average (0.48). In order to
foster the innovation forces that are key for thriving in the digitized world and the emerging, so called, Fourth
Industrial Revolution, many governments in the region will urgently need to reinforce efforts to improve their
regulatory and innovation environments (WEF, 2016). Finally, sub-Saharan African countries are among the
last positions in the ranking. Algeria and Cameroon are the last ones in the table.
Regarding index heterogeneity, the performance of each country across dimensions is unequal, as can be
seen in the Table 5, which displays the coefficient of variation for the scores obtained on each dimension of the
DiGIX, for each country present in the sample.
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Table 4
Digitalization Index
1 Luxembourg 1.00 46 Kazakhstan 0.47 91 Pakistan 0.16
2 United Kingdom 0.97 47 South Africa 0.47 92 Paraguay 0.15
3 Hong Kong SAR 0.95 48 Slovakia 0.46 93 Zimbabwe 0.13
4 United States 0.92 49 Mauritius 0.46 94 Bangladesh 0.12
5 Netherlands 0.90 50 Colombia 0.45 95 Côte d'Ivoire 0.11
6 Japan 0.88 51 Russian Federation 0.45 96 Zambia 0.10
7 Singapore 0.87 52 Italy 0.44 97 Bolivia 0.07
8 Norway 0.86 53 Azerbaijan 0.44 98 Nicaragua 0.06
9 Finland 0.85 54 Poland 0.43 99 Cameroon 0.05
10 Sweden 0.84 55 Romania 0.43 100 Algeria 0.00
11 Switzerland 0.82 56 Croatia 0.43 12 Iceland 0.82 57 Montenegro 0.42
13 Canada 0.81 58 Kuwait 0.41
14 New Zealand 0.80 59 Mexico 0.41
15 Australia 0.79 60 Greece 0.40
16 Germany 0.78 61 Armenia 0.40
17 Denmark 0.77 62 Georgia 0.40
18 Korea, Rep. 0.76 63 Panama 0.40
19 Estonia 0.76 64 Macedonia FYR 0.39
20 France 0.76 65 China 0.39
21 Austria 0.73 66 Thailand 0.38
22 United Arab Emirates 0.71 67 Morocco 0.38
23 Belgium 0.69 68 Philippines 0.37
24 Ireland 0.68 69 Sri Lanka 0.35
25 Israel 0.68 70 Egypt 0.34
26 Bahrain 0.65 71 Indonesia 0.33
27 Lithuania 0.65 72 Bulgaria 0.33
28 Malta 0.64 73 Moldova 0.33
29 Malaysia 0.63 74 Tunisia 0.33
30 Spain 0.62 75 Argentina 0.33
31 Qatar 0.61 76 Kenya 0.32
32 Saudi Arabia 0.59 77 Peru 0.32
33 Portugal 0.59 78 El Salvador 0.32
34 Chile 0.58 79 Serbia 0.31
35 Latvia 0.55 80 Dominican Rep. 0.31
36 Czech Republic 0.52 81 Vietnam 0.31
37 Oman 0.51 82 Honduras 0.30
38 Turkey 0.50 83 India 0.29
39 Costa Rica 0.49 84 Albania 0.26
40 Jordan 0.49 85 Senegal 0.24
41 Cyprus 0.48 86 Guatemala 0.24
42 Hungary 0.48 87 Ukraine 0.22
43 Uruguay 0.48 88 Botswana 0.21
44 Brazil 0.48 89 Nigeria 0.18
45 Slovenia 0.47 90 Lebanon 0.18
Source: BBVA Research
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In order to compare our index with some variables of interest and alternative approaches in the literature, we
compute some correlation analysis. Firstly, we observe that the correlation between the DiGiX and per capita
income is relatively strong, 70%. Secondly, we compare our index with other in the literature such as the NRI.
We compute standard and rank correlations to compare the two indices calculated with different
methodologies. Table 5 shows the Pearson and Spearman correlations. Since the scores in our index have no
direct interpretation, we focus on Spearman correlation between rankings. This coefficient gauges the
correlation extent between two variables according the hierarchy or range within a sample. Its main advantage
lies in the fact that is a non-parametric technique, which makes it independent from the statistical distributions
of the subject variables of study. In this case, it is appropriate to assess the extent of coincidence between two
rankings: DiGiX and the Networked Readiness Index. It is equivalent to the Pearson Correlation applied to the
range of two variables within their sample set.1 Given the degree of coincidence between both rankings, the
Spearman coefficient suggests a similar conclusion. Only for Africa, it is slightly below 0,9.
Considering the individual analysis of each dimension, it is interesting the lower correlation between the cost
dimensions. One potential explanation might be the lack of the prepaid mobile cellular tariffs in the DiGix,
which is included in the NRI. The exclusion of this variable in the DiGix is due to the scarce use of this kind of
tariffs in several countries in the sample, specially developed countries. Thus, we consider these data as non-
representative of the service supply.
Table 5
Spearman and Pearson Correlation Coefficients
Source: BBVA Research
5. Conclusions
The DiGiX assesses the factors, agents’ behaviour and institutions that enable a country to fully leverage
information and communication technologies (ICTs) for increasing adoption of digital services, competitiveness
and well-being. The DiGiX measures, on a scale from 0 (worst) to 1 (best), the digital performance of 100
countries, including developed and less developed countries. It is made of 21 indicators grouped in 6
dimensions and it is computed on annual basis. Due to the dynamic nature of the fields considered in the
DiGiX, small changes are possible in future editions of the index due to technology variations.
1: The Pearson correlation coefficient shows a strong relation between the DiGiX and the NRI (see Table XX). If we divide our sample into regions, for most of them, this coefficient is near 1, and in any case is greater than 0.9.
Pearson Spearman Pearson Spearman Pearson Spearman Pearson Spearman Pearson Spearman
OVERALL INDEX 0.97 0.97 0.95 0.95 0.94 0.95 0.92 0.88 0.97 0.98
INFRAESTR 0.72 0.89 0.52 0.80 0.62 0.63 0.70 0.65 0.65 0.93
USERS ADOPTION 0.99 0.99 0.97 0.96 0.96 0.92 0.96 0.94 0.99 0.98
ENPERPRISES ADOPT 0.88 0.94 0.83 0.86 0.95 0.93 0.86 0.81 0.88 0.95
COST 0.68 0.57 0.45 0.31 0.46 0.67 0.74 0.76 0.72 0.59
REGULATION 0.97 0.96 0.97 0.97 0.93 0.86 0.97 0.93 0.97 0.97
CONTENT 0.85 0.86 0.79 0.77 0.89 0.84 0.75 0.73 0.88 0.89
World OECD Latam África Asia
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The computation of the DiGiX is based on the common information aggregations of individual indicators, from
the indicator level (i.e., the most disaggregated level) to the overall DiGiX score (i.e., the highest level).
Our index allows several types of analysis such as comparative analysis among countries (regions) and
individual analysis by dimension, which is useful for policy making. Measuring the economic and social impact
of the digital economy is important for making appropriate policy decisions in both developed and developing
economies.
The limitation of our index now is comparability with previous years since the usage of new technology-related
indicators, such as 3G technologies, was only recently adopted by some countries.
Figure 2
DiGiX: Comparison among countries
Source: BBVA Research
6. References
The European Commission. The Digital Economy & Society Index (DESI) | Digital Single Market. 2015
Golinski, M. Measuring the information society-state of the art. International Journal of Digital Information and
Wireless Communications (IJDIWC), 1(2), 314-331. 2011
Corrocher, N., & Ordanini, A. Measuring the digital divide: A framework for the analysis of cross-country
differences. Journal of Information technology, 17(1), 9-19. 2002
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7. References Appendix A: Data Sources
World Telecommunication/ICT Indicators database (ITU) 2016
The World Telecommunication/ICT Indicators database contains time series data for the years 1960, 1965,
1970 and annually from 1975 to 2014. These data are available for over 200 economies; however the
availability of data for the different indicators and years can vary. The data are collected from an annual
questionnaire sent to official economy contacts, usually the regulatory authority or the ministry in charge of
telecommunication and ICT. Additional data are obtained from reports provided by telecommunication
ministries, regulators and operators and from ITU staff reports.
World Development Indicators - World Bank 2016
Executive Opinion Survey (World Economic Forum)
The World Economic Forum has conducted its annual Survey for over 30 years, making it the longest-
running and most extensive survey of its kind. The Survey is administered each year in over 140
economies. It captures valuable information on a broad range of factors that are critical for a country’s
competitiveness and sustainable development and for which data sources are scarce or, frequently,
nonexistent on a global scale. Among several examples of otherwise-unavailable data are the quality of the
educational system. indicators measuring business sophistication. and labor market variables such as
flexibility in wage determination
Doing Business 2016
Launched in 2002, looks at domestic small and medium-size companies and provides objective measures
of business regulations and their enforcement across countries and selected cities at the subnational and
regional level. These reports provide data on the ease of doing business, rank each location, and
recommend reforms to improve performance in each of the indicator areas. The first Doing Business report,
published in 2003, covered 5 indicator sets and 133 economies. This year’s report covers 11 indicator sets
and 189 economies.
United Nations (UN)
Business Software Alliance (BSA)
International Monetary Fund (IFS, IMF)
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Appendix B: Variable descriptions
Infrastructure subindex:
I1_3gcoverage: ITU - Percentage of the population covered by at least a 3G mobile network. Percentage
of the population covered by at least a 3G mobile network refers to the percentage of inhabitants that are
within range of at least a 3G mobile-cellular signal; irrespective of whether or not they are subscribers. This
is calculated by dividing the number of inhabitants that are covered by at least a 3G mobile-cellular signal
by the total population and multiplying by 100.
I2_bandwidth: ITU - International Internet bandwidth (bit/s) per Internet user. International Internet
bandwidth refers to the capacity that backbone operators provide to carry Internet traffic. It is measured in
bits per second per Internet users.
I3_secservers: WB - Secure Internet servers (per 1 million people). Secure servers are servers using
encryption technology in Internet transactions.
I4_bandwidth2: ITU- International Internet bandwidth in Mbit/s. International Internet bandwidth refers to
the total used capacity of international Internet bandwidth in megabits per second (Mbit/s). It is measured
as the sum of used capacity of all Internet exchanges (locations where Internet traffic is exchanged)
offering international bandwidth. If capacity is asymmetric (i.e. more incoming (downlink) than outgoing
(uplink) capacity) then the incoming (downlink) capacity should be provided.
Households’ adoption subindex:
AU1_mbroadband: ITU - Active mobile-broadband subscriptions per 100 inhabitants. Active mobile-
broadband subscriptions refer to the sum of standard mobile-broadband and dedicated mobile-broadband
subscriptions to the public Internet. It covers actual subscribers not potential subscribers. even though the
latter may have broadband enabled-handsets.
AU2_fbroadband: ITU - Fixed (wired)-broadband subscriptions per 100 inhabitants. Refers to
subscriptions to high-speed access to the public Internet (a TCP/IP connection) at downstream speeds
equal to or greater than 256 kbit/s. This includes cable modem DSL fibre-to-the-home/building and other
fixed (wired)-broadband subscriptions. This total is measured irrespective of the method of payment. It
excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular
networks. It should exclude technologies listed under the wireless-broadband category.
AU3_socnetworks: UN - Use of virtual social networks (1-7). In your country. how widely used are virtual
social networks (e.g.. Facebook. Twitter. LinkedIn)? [1 = not used at all; 7 = widely used]
AU4_inthomes: ITU - Percentage of households with Internet. Refers to the percentage of households with
Internet access at home.
AU5_intpeople: ITU - Percentage of individuals using the Internet. Refers to the proportion of individuals
that used the Internet in the last 12 months.
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Enterprises’ adoption subindex:
AE1_bbint: UN - Business-to-business Internet use (1-7). In your country to what extent do businesses
use ICTs for transactions with other businesses? [1 = not at all; 7 = to a great extent]
AE2_bcint: UN - Business-to-consumer Internet use (1-7). In your country to what extent do businesses
use Internet for selling their goods and services to consumers? [1 = not at all; 7 = to a great extent]
AE3_firmtech: UN - Firm-level technology absorption (1-7). In your country to what extent do businesses
adopt new technology? [1 = not at all; 7 = adopt extensively]
Cost subindex:
C1_fbroadband: UN - Fixed broadband Internet tariffs. PPP $/month. Monthly subscription charge for fixed
(wired) broadband Internet service (PPP $)Fixed (wired) broadband is considered any dedicated
connection to the Internet at downstream speeds equal to or greater than 256 kilobits per second using
DSL. The amount is adjusted for purchasing power parity (PPP) and expressed in current international
dollars. PPP figures were sourced from the World Bank's [i]World Development Indicators Online[i]
(December 2014) and the International Monetary Fund's [i]World Economic Outlook[i] (October 2014
edition). After computing the indicator, we divide by county GDP per capita in order to make it comparable
across countries. This variable is divided by the GDP pc PPP in order to make it comparable.
C2_intelcompetition: ITU - Internet & telephony competition, 0–2 (best). This variable measures the
degree of liberalization in 17 categories of ICT services, including 3G/4G telephony, international long
distance calls, and international gateways. For each economy, the level of competition in each of the
categories is assessed as follows: monopoly, partial competition, and full competition. The results reflect
the situation as of 2013 for the majority of countries (for others, data are available as of 2012 or earlier
years). The index is calculated as the average of points obtained in each of the 17 categories for which
data are available. Full liberalization across all categories yields a score of 2, the best possible score. For
more information, consult http://www.itu.int/ITU-D/ICTEYE/Reports.aspx.
Regulation subindex:
R1_ict: UN - Laws relating to ICTs (1-7). How developed are your country’s laws relating to the use of ICTs
(e.g., electronic commerce, digital signatures, consumer protection)? [1 = not developed at all; 7 =
extremely well-developed].
R2_softpiracy: UN - Software piracy rate, % software installed. This measure covers piracy of all
packaged software that runs on personal computers (PCs), including desktops, laptops, and ultra-portables,
including netbooks. This includes operating systems; systems software such as databases and security
packages; business applications; and consumer applications such as games, personal finance, and
reference software. The study does not include software that runs on servers or mainframes, or software
loaded onto tablets or smart phones.
R3_effectlaw: UN - Effectiveness of law-making bodies, 1-7 (best). How effective is your national
parliament/congress as a law-making institution? [1 = not effective at all—among the worst in the world; 7 =
extremely effective—among the best in the world].
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R4_judindep: UN - Judicial independence, 1-7 (best). In your country, to what extent is the judiciary
independent from influences of members of government, citizens, or firms? [1 = heavily influenced; 7 =
entirely independent].
R5_effilegalsystem1: UN - Efficiency of legal system in settling disputes, 1-7 (best). In your country, how
efficient is the legal framework for private businesses in settling disputes? [1 = extremely inefficient; 7 =
extremely efficient].
R6_effilegalsystem2: UN - Efficiency of legal system in challenging regs, 1-7 (best). In your country, how
easy is it for private businesses to challenge government actions and/or regulations through the legal
system? [1 = extremely difficult; 7 = extremely easy].
R7_ndayscontract: UN - No. days to enforce a contract. Time is recorded in calendar days, counted from
the moment the plaintiff decides to file the lawsuit in court until payment. This includes both the days when
actions take place and the waiting periods between.
R8_judicialquality: DB - Quality of judicial processes index (0-18). The quality of judicial processes index
measures whether each economy has adopted a series of good practices in its court system in four areas:
court structure and proceedings, case management, court automation and alternative dispute resolution.
The index is the sum of the scores of the four areas and ranges from 0 to 18, with higher values indicating
better and more efficient judicial processes.
Digital content subindex:
CO1_gov: UN - Government Online Service Index. 0–1 (best). The Government Online Service Index
assesses the quality of government’s delivery of online services on a 0-to-1 (best) scale. According to the
United Nations' Public Administration Network the Government Online Service Index captures a
government’s performance in delivering online services to the citizens. There are four stages of service
delivery. “Emerging”. “Enhanced”. “Transactional” and “Connected”. Online services are assigned to each
stage according to their degree of sophistication from the more basic to the more sophisticated. In each
country, the performance of the government in each of the four stages is measured as the number of
services provided as a percentage of the maximum services in the corresponding stage. Examples of
services include online presence, deployment of multimedia content, governments' solicitation of citizen
input, widespread data sharing and use of social networking. For more information about the methodology:
www2.unpan.org/egovkb/datacenter/CountryView.aspx.
16 / 17 www.bbvaresearch.com
Working Paper February 2017
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